Monthly Archives: May 2017

Bagging, the perfect solution for model instability

By Gabriel Vasconcelos Motivation The name bagging comes from boostrap aggregating. It is a machine learning technique proposed by Breiman (1996) to increase stability in potentially unstable estimators. For example, suppose you want to run a regression with a few … Continue reading

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Problems of causal inference after selecting controls

By Gabriel Vasconcelos Inference after model selection In many cases, when we want to estimate some causal relationship between two variables we have to solve the problem of selecting the right control variables. If we fail, our results will be … Continue reading

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